Deployment of software releases on datacenters configured in cloud platforms

ABSTRACT

Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system receives an artifact version map that identifies versions of software artifacts for datacenter entities of the datacenter and a cloud platform independent master pipeline that includes instructions for performing operations related to services on the datacenter, for example, deploying software artifacts, provisioning computing resources, and so on. The system compiles the cloud platform independent master pipeline in conjunction with the artifact version map to generate cloud platform specific detailed pipeline that deploys the appropriate versions of deployment artifacts on the datacenter entities in accordance with the artifact version map. The system sends the cloud platform specific detailed pipeline to a target cloud platform for execution.

BACKGROUND Field of Art

This disclosure relates in general to management of software releases incloud computing platforms, and in particular to a deployment of softwarereleases on data centers configured in cloud computing platforms.

Description of the Related Art

Organizations are increasingly replying on cloud platforms (or cloudcomputing platforms) such as AWS (AMAZON WEB SERVICES), GOOGLE cloudplatform, MICROSOFT AZURE, and so on for their infrastructure needs.Cloud platforms provide servers, storage, databases, networking,software, and so on over the internet to organizations. Conventionally,organizations maintained data centers that house hardware and softwareused by the organization. However, maintaining data centers can resultin significant overhead in terms of maintenance, personnel, and so on.As a result, organizations are shifting their data centers to cloudplatforms that provide scalability and elasticity of computingresources.

Organizations maintain cloud infrastructure on cloud platforms usingcontinuous delivery platforms that can manage and deploy applications oncloud platforms. Such continuous delivery platforms allow organizationsto simplify software deployment process and manage applications,firewalls, clusters, servers, load balancers, and other computinginfrastructure on the cloud platform. However, deploying softwarereleases for services provided on a cloud platform using a continuousdelivery platform can be complex. For example, different versions ofsoftware may have to be deployed on different services running ondifferent cloud computing resources. Furthermore, each cloud platformuses different tools for managing the resources.

A large system such as a multi-tenant systems may manage services for alarge number of organizations representing tenants of the multi-tenantsystem and may interact with multiple cloud platforms. A multi-tenantsystem may have to maintain several thousand such data centers on acloud platform. Each datacenter may have different requirements forsoftware releases. Furthermore, the software, languages, featuressupported by each cloud platform may be different. For example,different cloud platforms may support different mechanism forimplementing network policies or access control. As a result, amulti-tenant system has to maintain different implementations ofmechanisms for releasing and deploying services on datacenters,depending on the number of cloud platforms that are supported for datacenters. This results in high maintenance cost for the multi-tenantsystem for supporting software releases on data centers across multiplecloud platforms.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a system environment illustrating amulti-tenant system configuring data centers on cloud platformsaccording to an embodiment.

FIG. 2A is a block diagram illustrating the system architecture of adeployment module 210 according to an embodiment.

FIG. 2B illustrates the overall process for deploying software artifactsin a datacenter according to an embodiment.

FIG. 3 is a block diagram illustrating the architecture of a softwarerelease management module according to one embodiment.

FIG. 4 illustrates an example of a data center declarative specificationaccording to one embodiment.

FIG. 5 illustrates example data centers created on a cloud platformbased on a declarative specification according to one embodiment.

FIG. 6 is a block diagram illustrating generation of data centers oncloud platforms based on a declarative specification, according to oneembodiment.

FIG. 7 shows the overall process for generating pipelines for deploymentof software artifacts on datacenters configured on a cloud platformaccording to an embodiment.

FIG. 8 illustrates an example master pipeline according to anembodiment.

FIG. 9 shows the overall process executed by a stage for an environmentof the master pipeline on a cloud platform according to an embodiment.

FIG. 10 shows an example master pipeline according to an embodiment.

FIG. 11 shows an example master pipeline for a datacenter according toan embodiment.

FIG. 12 illustrates how the execution of the master pipeline is modifiedbased on an artifact version map according to an embodiment.

FIG. 13 shows the overall process for deployment of software artifactson datacenters configured on a cloud platform according to anembodiment.

FIG. 14 is a block diagram illustrating a functional view of a typicalcomputer system for use in the environment of FIG. 1 according to oneembodiment.

The figures depict various embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the structures and methodsillustrated herein may be employed without departing from the principlesof the embodiments described herein.

The figures use like reference numerals to identify like elements. Aletter after a reference numeral, such as “115 a,” indicates that thetext refers specifically to the element having that particular referencenumeral. A reference numeral in the text without a following letter,such as “115,” refers to any or all of the elements in the figuresbearing that reference numeral.

DETAILED DESCRIPTION

Cloud platforms provide computing resources, such as storage, computingresources, applications, and so on to computing systems on an on-demandbasis via a public network such as internet. Cloud platforms allowenterprises to minimize upfront costs to set up computing infrastructureand also allow enterprises to get applications up and running fasterwith less maintenance overhead. Cloud platforms also allow enterprisesto adjust computing resources to rapidly fluctuating and unpredictabledemands. Enterprises can create a data center using a cloud platform foruse by users of the enterprise. However, implementing a data center oneach cloud platform requires expertise in the technology of the cloudplatform.

Embodiments create data centers in a cloud platform using a cloudplatform infrastructure language that is cloud platform independent. Thesystem receives a cloud platform independent declarative specificationof a data center. The declarative specification describes the structureof the data center and may not provide instructions specifying how tocreate the data center. The cloud platform independent declarativespecification is configured to generate the data center on any of aplurality of cloud platforms and is specified using a cloud platforminfrastructure language. The system receives information identifying atarget cloud platform for creating the data center and compiles thecloud platform independent declarative specification to generate a cloudplatform specific data center representation. The system sends the cloudplatform specific data center representation and a set of instructionsfor execution on the target cloud platform. The target cloud platformexecutes the instructions to configure the data center using theplatform specific data center representation. The system provides userswith access to the computing resources of the data center configured bythe cloud platform.

In one embodiment, the system performs operations related to softwarereleases on datacenters configured on a cloud platform, for example,deploying software releases, provisioning resources, performing rollbackof software releases, and so on. The system accesses a data centerconfigured on a target cloud platform. The datacenter is generated basedon a cloud platform independent declarative specification comprising ahierarchy of data center entities. Each data center entity comprises oneor more of (1) a service or (2) one or more other data center entities.The system generates a cloud platform independent master pipeline thatcomprises: (1) a sequence of stages for deployment of a softwareartifact, for example, a development stage, a test stage, and aproduction stage, and (2) criteria for promoting the software artifactfrom one stage to a subsequent stage of the sequence of stages. Thesystem compiles the cloud platform independent master pipeline togenerate a cloud platform dependent detailed pipeline for the targetcloud platform with instructions for performing operations related toservices according to the layout of datacenter defined by thedeclarative specification. The system executes the cloud platformdependent detailed pipeline on the target cloud platform, for example,to deploy software releases on datacenter entities of the datacenter.

In one embodiment, the system accesses the data center configured on atarget cloud platform. The system receives a cloud platform independentartifact version map associating data center entities of the data centerwith versions of software artifacts targeted for deployment on thedatacenter entities. Each software artifact comprises executableinstructions associated with a service configured for execution on oneor more cloud platforms. The system generates a cloud platform specificmaster pipeline for the target cloud platform based on the cloudplatform independent artifact version map. The cloud platform specificmaster pipeline comprises instructions to perform operations such asbuild and deploy appropriate versions of deployment artifacts forservices on data center entities in accordance with the cloud platformindependent version map. The system transmits the cloud platformspecific deployment pipeline to the target cloud platform for execution.The artifact version map and the master pipelines can be used to performvarious actions related to services including deployment of service,destroying services, provisioning resources for services, destroyingresources for services, and so on.

A cloud platform is also referred to herein as a substrate. Thedeclarative specification of data center is substrate independent orsubstrate agnostic. If operations related to a datacenter such asdeployment of software releases, provisioning of resources, and so onare performed using conventional techniques, the user has to providecloud platform specific instructions. Accordingly, the user needsexpertise of the cloud platform being used. Furthermore, theinstructions are cloud platform specific and are not portable acrossmultiple platforms. For example, the instructions for deploying softwareon an AWS cloud platform are different from instructions on a GCP cloudplatform. A developer needs to understand the details of how eachfeature is implemented on that specific cloud platform. The systemdisclosed provides a cloud platform infrastructure language that allowsusers to perform operations on datacenters using instructions that arecloud platform independent and can be executed on any cloud platformselected from a plurality of cloud platforms. A compiler of the cloudplatform infrastructure language generates a cloud platform specificdetailed instructions for a target cloud platform.

The cloud platform infrastructure language may be referred to as adomain specific language (DSL). The system may represent a multi-tenantsystem but is not limited to multi-tenant systems and can be any onlinesystem or any computing system with network access to the cloudplatform.

System Environment

FIG. 1 is a block diagram of a system environment illustrating amulti-tenant system configuring data centers on cloud platformsaccording to an embodiment. The system environment 100 comprises amulti-tenant system 110, one or more cloud platforms 120, and one ormore client devices 105. In other embodiments, the system environment100 may include more or fewer components.

The multi-tenant system 110 stores information of one or more tenants115. Each tenant may be associated with an enterprise that represents acustomer of the multi-tenant system 110. Each tenant may have multipleusers that interact with the multi-tenant system via client devices 105.

A cloud platform may also be referred to as a cloud computing platformor a public cloud environment. A tenant may use the cloud platforminfrastructure language to provide a declarative specification of adatacenter that is created on a target cloud platform 120 and to performoperations using the datacenter, for example, provision resources,perform software releases and so on. A tenant 115 may create one or moredata centers on a cloud platform 120. A data center represents a set ofcomputing resources including servers, applications, storage, memory,and so on that can be used by users, for example, users associated withthe tenant. Each tenant may offer different functionality to users ofthe tenant. Accordingly, each tenant may execute different services onthe datacenter configured for the tenant. The multi-tenant system mayimplement different mechanisms for release and deployment of softwarefor each tenant. A tenant may further obtain or develop versions ofsoftware that include instructions for various services executing in adatacenter. Embodiments allow the tenant to deploy specific versions ofsoftware releases for different services running on different computingresources of the datacenter.

The computing resources of a data center are secure and may not beaccessed by users that are not authorized to access them. For example, adata center 125 a that is created for users of tenant 115 a may not beaccessed by users of tenant 115 b unless access is explicitly granted.Similarly, data center 125 b that is created for users of tenant 115 bmay not be accessed by users of tenant 115 a, unless access isexplicitly granted. Furthermore, services provided by a data center maybe accessed by computing systems outside the data center, only if accessis granted to the computing systems in accordance with the declarativespecification of the data center.

With the multi-tenant system 110, data for multiple tenants may bestored in the same physical database. However, the database isconfigured so that data of one tenant is kept logically separate fromthat of other tenants so that one tenant does not have access to anothertenant's data, unless such data is expressly shared. It is transparentto tenants that their data may be stored in a table that is shared withdata of other customers. A database table may store rows for a pluralityof tenants. Accordingly, in a multi-tenant system, various elements ofhardware and software of the system may be shared by one or moretenants. For example, the multi-tenant system 110 may execute anapplication server that simultaneously processes requests for a numberof tenants. However, the multi-tenant system enforces tenant-level dataisolation to ensure that jobs of one tenant do not access data of othertenants.

Examples of cloud platforms include AWS (AMAZON web services), GOOGLEcloud platform, or MICROSOFT AZURE. A cloud platform 120 offerscomputing infrastructure services that may be used on demand by a tenant115 or by any computing system external to the cloud platform 120.Examples of the computing infrastructure services offered by a cloudplatform include servers, storage, databases, networking, security, loadbalancing, software, analytics, intelligence, and other infrastructureservice functionalities. These infrastructure services may be used by atenant 115 to build, deploy, and manage applications in a scalable andsecure manner.

The multi-tenant system 110 may include a tenant data store that storesdata for various tenants of the multi-tenant store. The tenant datastore may store data for different tenants in separate physicalstructures, for example, separate database tables or separate databases.Alternatively, the tenant data store may store data of multiple tenantsin a shared structure. For example, user accounts for all tenants mayshare the same database table. However, the multi-tenant system storesadditional information to logically separate data of different tenants.

Each component shown in FIG. 1 represents one or more computing devices.A computing device can be a conventional computer system executing, forexample, a Microsoft™ Windows™-compatible operating system (OS), Apple™OS X, and/or a Linux distribution. A computing device can also be aclient device having computer functionality, such as a personal digitalassistant (PDA), mobile telephone, video game system, etc. Eachcomputing device stores software modules storing instructions.

The interactions between the various components of the systemenvironment 100 are typically performed via a network, not shown inFIG. 1. In one embodiment, the network uses standard communicationstechnologies and/or protocols. In another embodiment, the entities canuse custom and/or dedicated data communications technologies instead of,or in addition to, the ones described above.

Although the techniques disclosed herein are described in the context ofa multi-tenant system, the techniques can be implemented using othersystems that may not be multi-tenant systems. For example, an onlinesystem used by a single organization or enterprise may use thetechniques disclosed herein to create one or more data centers on one ormore cloud platforms 120.

System Architecture

The multi-tenant system 110 includes a deployment module for deployingsoftware artifacts on the cloud platforms. The deployment module canperform various operations associated with software releases, forexample, provisioning resources on a cloud platform, deploying softwarereleases, performing rollbacks of software artifacts installed ondatacenter entities, and so on. FIG. 2 is a block diagram illustratingthe system architecture of a deployment module 210 according to anembodiment. The deployment module 210 includes a data center generationmodule 220 and a software release management module 230. Otherembodiments can have different and/or other components than the onesdescribed here, and that the functionalities can be distributed amongthe components in a different manner.

The data center generation module 220 includes instructions for creatingdatacenters on the cloud platform. The software release managementmodule 230 includes instructions for deploying software releases forvarious services or applications running on the datacenters created bythe data center generation module 220.

The data center generation module 220 receives from users, for example,users of a tenant, a cloud platform independent declarativespecification of a data center. The cloud platform independentdeclarative specification of a data center specifies various entities ofthe data center. In an embodiment, the cloud platform independentdeclarative specification of a data center comprises a hierarchicalorganization of datacenter entities, where each datacenter entity maycomprise one or more services, one or more other datacenter entities ora combination of both. FIG. 4 describes various types of datacenterentities in further detail. The data center generation module 220receives the platform independent declarative specification and a targetcloud platform as input and generates a cloud platform specific metadatarepresentation for the target cloud platform. The data center generationmodule 220 deploys the generated cloud platform specific metadatarepresentation on the target cloud platform to create a data center onthe target cloud platform according to the declarative specification.

The software release management module 230 receives as inputs (1) anartifact version map 225 and (2) a master pipeline 235. The artifactversion map 225 identifies specific versions of software releases ordeployment artifacts that are targeted for deployment on specificdatacenter entities. The artifact version map 225 maps datacenterentities to software release versions that are targeted to be deployedon the datacenter entities. The master pipeline 235 includesinstructions for operations related to software releases on thedatacenter, for example, deployment of services, destroying services,provisioning resources for services, destroying resources for services,and so on.

The master pipeline 235 may include instructions for performingoperations related to software releases for different environments suchas development environment, test environment, canary environment, andproduction environment, and instructions for determining when a softwarerelease is promoted from one environment to another environment. Forexample, if the deployments of a software release in a developmentenvironment execute more than a threshold number of test cases, thesoftware release is promoted for test environment for further testing,for example, system level and integration testing. If the softwarerelease in a test environment passes a threshold of test coverage, thesoftware release is promoted to canary environment where the softwarerelease is provided to a small subset of users on a trial basis. If thesoftware release in a canary environment executes without errors for athreshold time, the software release is promoted to productionenvironment where the software release is provided to all users.

The software release management module 230 compiles the input artifactversion map 225 and the master pipeline 235 to generate a cloud platformspecific detailed pipeline 255 that is transmitted to the target cloudplatform. The cloud platform specific detailed pipeline 255 includesinstructions for deploying the appropriate version of a software releaseor deployment artifact on the datacenter entities as specified in theartifact version map 225. The software release management module 230 mayreceive modifications to one of the inputs. For example, a user maymodify the input artifact version map 225 and provide the same masterpipeline 235. Accordingly, the same master pipeline is being used butdifferent software releases are being deployed on datacenter entities.The software release management module 230 recompiles the inputs togenerate a new cloud platform specific detailed pipeline 255 thatdeploys the versions of software releases according to the new artifactversion map 225.

The artifact version map may also be referred to as a deploymentmanifest, a version manifest, a software release map, or a softwareartifact version map. The master pipeline may also be referred to as amaster deployment pipeline or a master orchestration pipeline.

FIG. 2B illustrates the overall process for deploying software artifactsin a datacenter according to an embodiment. FIG. 2B shows a layout of adatacenter 265 including various datacenter entities. As shown in FIG.2B, the artifact version map 225 identifies the different versions ofsoftware that are targeted for release on different datacenter entities275 of the datacenter 265. The master pipeline represents the flow ofdeployment artifacts through the various environments of the datacenter.The software release management module 230 combines the information inthe master pipeline 235 with the artifact version map 225 to determinecloud platform specific detailed pipeline 255 that maps the appropriateversion of software artifacts on the datacenter entities according tothe artifact version map 225.

FIG. 3 is a block diagram illustrating the architecture of a softwarerelease management module 230 according to one embodiment. The softwarerelease management module 230 includes a parsing module 310, a pipelinegenerator module 320, an artifact version map store 330, a pipelinestore 340, and a pipeline execution engine 360. Other embodiments mayinclude more, fewer, or different modules than those indicated herein inFIG. 3.

The parsing module 310 parses various types of user input includingdeclarative specification of a data center, artifact version map 225,and master pipelines 235. The parsing module 310 generates datastructures and metadata representations of the input processed andprovides the generated data structures and metadata representations toother modules of the software release management module 230 for furtherprocessing.

The metadata store 340 stores various transformed metadatarepresentations of data centers that are generated by the softwarerelease management module 230. The transformed metadata representationsmay be used for performing rollback to a previous version if an issue isencountered in a current version of the data center. The transformedmetadata representations may be used for validation, auditing,governance, and so on at various stages of the transformation process.

The pipeline generator module 320 processes the master pipelines inconjunction with the artifact version map received as input to generatea detailed pipeline for a target cloud platform. The pipelines comprisestages that include instructions for provisioning services or deployingapplications for deploying versions of software releases for variousservices on the cloud platform according to the artifact version map.The artifact version map store 330 stores artifact version maps receivedfrom users and the pipeline store 340 stores master pipelines as well aspipelines generated by the pipeline generator module 320.

The pipeline execution engine 360 executes the detailed pipelinesgenerated by the pipeline generator module 320. In an embodiment, thepipeline execution engine 360 is a system such as SPINNAKER thatexecutes pipelines for releasing/deploying software. The pipelineexecution engine 360 parses the pipelines and executes each stage of thepipeline on a target cloud computing platform.

Cloud Platform-Based Data Center Generation

FIG. 4 illustrates an example of a declarative specification of a datacenter according to one embodiment. The declarative specification 410includes multiple data center entities. A data center entity is aninstance of a data center entity type and there can be multipleinstances of each data center entity type. Examples of data centerentities include data centers, service groups, services, teams,environments, and schemas.

The declarative specification 410 includes definitions of various typesof data center entities including service group, service, team,environment, and schema. The declarative specification includes one ormore instances of data centers. Following is a description of varioustypes of data center entities and their examples. The examples areillustrative and show some of the attributes of the data centerentities. Other embodiments may include different attributes and anattribute with the same functionality may be given a different name thanthat indicated herein. In an embodiment, the declarative specificationis specified using hierarchical objects, for example, JSON (Javascriptobject notation) that conform to a predefined schema.

A service group 520 represents a set of capabilities and features andservices offered by one or more computing systems that can be built anddelivered independently, in accordance with one embodiment. A servicegroup may be also referred to as a logical service group, a functionalunit, or a bounded context. A service group 520 may also be viewed a setof services of a set of cohesive technical use-case functionalitiesoffered by one or more computing systems. A service group 520 enforcessecurity boundaries. A service group 520 defines a scope formodifications. Thus, any modifications to an entity, such as acapability, feature, or service offered by one or more computing systemswithin a service group 520 may propagate as needed or suitable toentities within the service group, but does not propagate to an entityresiding outside the bounded definition of the service group 520. A datacenter may include multiple service groups 520. A service groupdefinition specifies attributes including a name, description, anidentifier, schema version, and a set of service instances. An exampleof a service group is a blockchain service group that includes a set ofservices used to providing blockchain functionality. Similarly, asecurity service group provides security features. A user interfaceservice group provides functionality of specific user interfacefeatures. A shared document service group provides functionality ofsharing documents across users. Similarly, there can be several otherservice groups.

Service groups support reusability of specification so that tenants orusers interested in developing a data center have a library of servicegroups that they can readily use. The boundaries around services of aservice groups are based on security concerns and network concerns amongothers. A service group is associated with protocols for performinginteractions with the service group. In an embodiment, a service groupprovides a collection of APIs (application programming interfaces) andservices that implement those APIs. Furthermore, service groups aresubstrate independent. A service group provides a blast radius scope forthe services within the service group so that any failure of a servicewithin the service group has impact limited to services within theservice group and has minimal impact outside the service group.

Following is an example of a specification of a service group. Theservice group specifies various attributes representing metadata of theservice group and includes a set of services within the service group.There may be other types of metadata specified for a service group, notindicated herein.

{  “service_group”: [  {   “cells”: [ ],   “description”: “Service groupService Instance Definitions”,   “service_group_id”: “id1”,   “name”:“name1”,   “schema_version”: “1.0”,   “cluster_instances”: [     {      “cluster_instance_name”: “cluster1”,       “cluster_type”:“cluster_type1”      },      {         “cluster_instance_name”: “cluster2”,         “cluster_type”: “ cluster_type1”      },      {        “cluster_instance_name”: “ cluster3”,         “cluster_type”: “cluster_type2”      }     ],   “service_instances”: [     {     “service_instance_name”: “serviceinstance0001”,     “service_type”: “servicetype1”     },     {     “service_instance_name”: “serviceinstance0002”,     “service_type”: “ servicetype1”      “cluster_instance”: “cluster1”    },     {      “service_instance_name”: “serviceinstance0003”,     “service_type”: “ servicetype2”     },     ...     ],   “service_teams”: [“team1”],    “type”: “servicetype”   “security_groups”:[     {       “name”:“group1”,       “policies”:[        {          “description”:“Allow access from site S1”,         “destination”:{ “groups”:[ “group2” ] },         “environments”:[ “dev”, “test”, “staging”],          “source”:{          “iplist”:“URL1”,           “filters”:[ filter-expression” ]        }        ]      }     ]   }  ] }

As shown in the example above, a service group may specify a set ofclusters. A cluster represents a set of computing nodes, for example, aset of servers, a set of virtual machines, or a set of containers (suchas KUBERNETES containers). A physical server may run multiplecontainers, where each container has its own share of filesystem, CPU,memory, process space, and so on.

The service group specifies a set of services. A service group mayspecify a cluster for a service so that the data center deployed on acloud platform runs clusters of computing nodes and maps the services toclusters based on the specified mapping if included in the declarativespecification. For example, in the service group example shown above,the service instance serviceinstance0002 is specified to run on clusterinstance cluster1.

The service group may specify security groups, each security groupspecifying a set of services that are allowed to interact with eachother. Services outside the security group are required to passadditional authentication to communicate with services within thesecurity group. Alternatively, the services within a security group useone protocol to interact with each other and services outside thesecurity group use a different protocol that requires enhancesauthentication to interact with services within the security group.Accordingly, a security group specifies policies that determine howservices can interact with each other. A security policy may specify oneor more environments for which the security policy is applicable. Forexample, a security policy policy1 may apply to a particular environmentenv1 (e.g., production environment) and another security policy policy2may apply to another environment env2 (e.g., development environment). Asecurity policy may be specified for a service group type or for aspecific service type.

In an embodiment, the security policy specifies expressions forfiltering the service groups based on various attributes so that thesecurity policy is applicable to the filtered set of service groups. Forexample, the security policy may specify a list of IP (internetprotocol) addresses that are white listed for a set of service groupsidentified by the filtered set and accordingly these computing systemsare allowed access to the service group or to specific set of serviceswithin the service group.

In an embodiment, a security policy may specify for a service group, aset of source services and a set of destination services. The sourceservices for a particular service specify the services outside thesecurity group that are allowed to connect with this particular service.The destination services for a particular service specify the servicesoutside the security group that this particular service needs to connectto. During provisioning and deployment, the data center generationmodule generates instructions for the cloud platform that implementspecific network policies using cloud platform specific features andnetwork functionality such that the network policies implement thesecurity policies specified in the declarative specification.

A data center entity called a cell represents a set of services thatinteract with each other in a vertical fashion and can be scaled byadditional instances or copies of the cell, i.e., copies of the set ofservices. Creating multiple instances of a cell allows a system to scalea set of services that interact with each other. A data center instancemay include one or more cells. Each cell may include one or moreservices. A data center may include instances of service groups orcells.

A service definition specifies metadata for a type of service, forexample, database service, load balancer service, and so on. Themetadata be describe various attributes of a service including a name ofthe service, description of the service, location of documentation forthe service, any sub-services associated with the service, an owner forthe service, a team associated with the service, build dependencies forthe service specifying other services on which this service depends atbuild time, start dependencies of the service specifying the otherservices that should be running when this particular service is started,authorized clients, DNS (domain name server) name associated with theservice, a service status, a support level for the service, and so on.The service definition specifies a listening ports attribute specifyingthe ports that the service can listen on for different communicationprotocols, for example, the service may listen on a port p1 for UDPprotocol and a port p2 for TCP protocol. Other services within the datacenter can interact with a service via the ports specified by theservice.

The service definition specifies an attribute outbound access thatspecifies destination endpoints, for example, external URLs (uniformresource locators) specifying that the service needs access to thespecified external URLs. During deployment, the data center generationmodule ensures that the cloud platform implements access policies suchthat instances of this service type are provided with the requestedaccess to the external URLs.

The outbound access specification may identify one or more environmenttypes for the service for which the outbound access is applicable. Forexample, an outbound access for a set of endpoints S1 may apply to aparticular environment env1 (e.g., production environment) and outboundaccess for a set of endpoints S2 may apply to another environment env2(e.g., development environment).

Following is an example of a service definition.

{  “service_definition”: [   {    “authorized_clients”: [ ],   “build_dependencies”: [ ],    “description”: “description ofservice”,    “dns_name”: “dns1”,    “documentation”: “URL”,    “name”:“name1”,    “namespace”: “space1”,    “service_owner”: “user1”,   “service_status”: “GA”,    “service_team”: “team1”,   “support_level”: “STANDARD”,    “start_dependencies”: [“svc5”,“svc7”, ...],    “sub_services”: [ “service1”, “ service2”, “ service3”, ...  ],     “listening_ports”:[        { “protocol”:“tcp”, “ports”:[“53” ] },        { “protocol”:“udp”,“ports”:[ “53” ] }     “outbound_access”:[         {          “destination”:[           {           “endpoints”:[ “.xyz.com:443”, “.pqr.com:443” ]           }         ]         }       ],   }  ] }

A team definition 450 includes team member names and other attributes ofa team for example, name, email, communication channel and so on.Following is an example of a team definition. A service may beassociated with one or more teams that are responsible to modificationsmade to that service. Accordingly, any modification made to that serviceis approved by the team. A service may be associated with a teamresponsible for maintenance of the service after it is deployed in acloud platform. A team may be associated with a service group and iscorrespondingly associated with all services of that service group. Forexample, the team approves any changes to the service group, forexample, services that are part of the service group. A team may beassociated with a data center and is accordingly associated with allservice groups within the data center. A team association specified at adata center level provides a default team for all the service groupswithin the data center and further provides a default team for allservices within the service groups.

According to an embodiment, a team association specified at thefunctional level overrides the team association provided at the datacenter level. Similarly, a team association specified at the servicelevel overrides the default that may have been provided by a teamassociation specified at the service group level or a data center level.A team can decide how certain action is taken for the data center entityassociated with the team. The team associations also determine thenumber of accounts on the cloud platform that are created for generatingthe final metadata representation of the data center for a cloudplatform by the compiler and for provisioning and deploying the datacenter on a cloud platform. The data center generation module 210creates one or more user accounts in the cloud platform and providesaccess to the team members to the user accounts. Accordingly, the teammembers are allowed to perform specific actions associated with the datacenter entity associated with the team, for example, making or approvingstructural changes to the data center entity or maintenance of the datacenter entity when it is deployed including debugging and testing issuesthat may be identified for the data center entity.

Conventional techniques associate the same team with the data centerthrough out the design process thereby resulting in the organizationalstructure having an impact on the design of the data center or servicegroup. Embodiments decouple the team definition from the constructionsthat define the data center entity, thereby reducing the impact of theteams on the design and architecture of the data center entity.

  {  “team_definition”: [   {    “name”: “team1”,    “description”:“description of team”,    “admins”: [     “user1”,     “user2”,    “user3”,     “user4”,     ...    ],    “team_id”: “id1”,    “owner”:“owner_id”,    “email”: “team1@xyz.com”,   }  ], “communication_channel”: “channel1”  “schema_version”: “1.0” }

An environment definition 460 specifies a type of system environmentrepresented by the data center, for example, development environment,staging environment, test environment, or production environment. Aschema definition 470 specifies schema that specifies syntax of specificdata center entity definitions. The schema definition 470 is used forvalidating various data center entity definitions. The data centergeneration module determines security policies for the data center inthe cloud platform specific metadata representation based on theenvironment. For example, a particular set of security policies may beapplicable for an environment env1 and a different set of securitypolicies may be applicable for environment env2. For example, thesecurity policies provide much more restricted access in productionenvironment as compared to development environment. The security policymay specify the length of time that a security token is allowed to existfor specific purposes. For example, long access tokens (e.g., week longaccess tokens) may be allowed in development environment but accesstokens with much smaller life time (e.g., few hours) used in productionenvironment. Access tokens may allow users or services with access tospecific cloud platform resources.

A data center definition 420 specifies the attributes and components ofa data center instance. A declarative specification may specify multipledata center instances. The data center definition 420 specifiesattributes including a name, description, a type of environment, a setof service groups, teams, domain name servers for the data center, andso on. A data center definition may specify a schema definition and anymetadata representation generated from the data center definition isvalidated against the specified schema definition. A data centerincludes a set of core services and capabilities that enable otherservices to function within the data center. An instance of a datacenter is deployed in a particular cloud platform and may be associatedwith a particular environment type, for example, development, testing,staging, production, and so on.

Following is a definition of a data center instance. The data centerinstance definition includes a list of service groups included in thedata center instance and other attributes including an environment ofthe data center, a data center identifier, a name, a region representinga geographical region, one or more teams associated with the datacenter, and a schema version.

  {  “datacenter_instance”: {     “environment”: “env1”,     “datacenter_instance_identifier”: “id1”,      “name”:“data_center1”,      “region”: “region1”,      “service_groups”: [      “service_group1”,       “ service_group2”,       “service_group3”,       “service_group4”,       ...      ],    “schema_version”: “1.0”,     “admin_team”:“admins”,     ...    }   } } }

FIG. 5 illustrates some example data centers created on a cloud platformbased on a declarative specification according to one embodiment. Thedata centers 510 may be created based on a declarative specificationprocessed by the data center generation module 210. As shown in FIG. 5,multiple data centers may be configured within a cloud platform 120.Each data center 510 may correspond to a tenant 115 of a multi-tenantsystem 110. A tenant 115 may create one or more data centers 510.Alternatively, a data center 510 may be created by any computing system.Each data center includes one or more service groups. For example, datacenter 510 a includes service groups 520 a and 520 b and data center 510b includes service group 520 c. A data center may include multipleinstances of a particular type of service group. Each service groupincludes a set of services. For example, service group 520 a includesservices 530 a and 530 b, service group 520 b includes services 530 a,530 b, and 530 c, and service group 520 c includes services 530 e, 530f, and 530 g. A service group may include multiple instances of servicesof the same service type.

The datacenter generation module 220 creates data centers on cloudplatforms based on a declarative specification using the followingsteps. The data center generation module 210 receives a cloud platformindependent declarative specification of a data center. The cloudplatform independent declarative specification may be for a tenant ofthe multi-tenant system or for any other computing system, for example,an online system. The cloud platform independent declarativespecification is specified using the cloud platform infrastructurelanguage. The cloud platform independent declarative specification ofthe data center is configured to generate the data center on any of aplurality of cloud platforms.

The data center generation module 210 receives information identifying atarget cloud platform for creating the data center based on the cloudplatform independent declarative specification. The target cloudplatform could be any of a plurality of cloud platforms, for example,AWS, AZURE, GCP, and so on. The data center generation module 210further receives information to connect with the target cloud platform,for example, credentials for creating a connection with the target cloudplatform. A cloud platform may also be referred to as a cloud computingplatform.

The data center generation module 210 compiles the cloud platformindependent declarative specification to generate a cloud platformspecific data center representation for creating the data center on thetarget cloud computing platform. For example, the cloud platformspecific data center representation may refer to user accounts, networkaddresses, and so on that are specific to the target cloud computingplatform.

The data center generation module 210 sends the platform specific datacenter representation along with instructions for deploying the datacenter on the target cloud computing platform. The target cloudcomputing platform executes the instructions to configure the computingresources of the target cloud computing platform to generate the datacenter according to the platform specific data center representation.The data center generation module 210 provides users with access to thecomputing resources of the data center configured by the cloud computingplatform. For example, if the data center was created for a tenant ofthe multi-tenant system, users associated with the tenant are providedwith access to the data center.

FIG. 6 is a block diagram illustrating generation of data centers oncloud platforms based on a declarative specification, according to oneembodiment. The data center generation module 210 receives as input acloud-platform independent declarative specification 610. Thecloud-platform independent declarative specification 610 may be aversion of the declarative specification that is being incrementallymodified by users. The data center generation module 210 processes aparticular version of the cloud-platform independent declarativespecification 610. Since cloud-platform independent declarativespecification 610 is not specified for any specific target cloudplatform, the data center generation module 210 can configure a datacenter on any target cloud platform based on the cloud-platformindependent declarative specification 610.

The data center generation module 210 processes the cloud-platformindependent declarative specification 610 to generate a cloud-platformindependent detailed metadata representation 620 for the data center.The cloud-platform independent detailed metadata representation 620defines details of each instance of data center entity specified in thecloud-platform independent declarative specification 610. The datacenter generation module 210 creates unique identifiers for data centerentity instances, for example, service instances.

In an embodiment, the cloud-platform independent detailed metadatarepresentation 620 includes an array of instances of data center entitytypes, for example, an array of service group instances of a particularservice group type. Each service group instance includes an array ofservice instances. A service instance may further include the details ofa team of users that are allowed to perform certain actions associatedwith the service instance. The details of the team are used duringprovisioning and deployment by the data center generation module 210,for example, for creating a user account for the service instance andallowing members of the team to access the user account.

The cloud-platform independent detailed metadata representation 620includes attributes of each instance of data center entity. Accordingly,the description of each instance of data center entity is expanded toinclude all details. As a result, the cloud-platform independentdetailed metadata representation 620 of a data center may besignificantly larger than the cloud-platform independent declarativespecification 610. For example, the cloud-platform independentdeclarative specification 610 may be few thousand lines ofspecification, whereas the cloud-platform independent detailed datacenter representation 620 may be millions of lines of generated code. Asa result, the data center generation module 210 keeps the cloud-platformindependent detailed metadata representation 620 as immutable, i.e.,once the representation is finalized, no modifications are performed tothe representation. For example, if any updates, deletes, or additionsof data center entities need to be performed, they are performed on thecloud platform independent declarative specification 610.

The data center generation module 210 receives a target cloud platformon which the data center is expected to be provisioned and deployed andgenerates a cloud platform specific detailed metadata representation 630of the data center. For example, the data center generation module 210interacts with the target cloud platform to generate certain entities(or resources), for example, user accounts, virtual private clouds(VPCs), and networking resources such as subnets on the VPCs, variousconnections between entities in the cloud platform, and so on. The datacenter generation module 210 receives resource identifiers of resourcesthat are created in the target cloud platform, for example, user accountnames, VPC IDs, and so on, and incorporates these in the cloud-platformindependent detailed metadata representation 620 to obtain the cloudplatform specific metadata representation 630 of the data center. In anembodiment, the data center generation module 210 creates one uniqueuser account on the cloud platform for each team for a given combinationof a service group and a service. The user account is used by the teamfor performing interactions with that particular service for thatservice group, for example, for debugging, for receiving alerts, and soon.

The target cloud platform may perform several steps to process thecloud-platform specific detailed metadata representation 630. Forexample, the cloud platform independent declarative specification mayspecify permitted interactions between services. These permittedinteractions are specified in the cloud-platform specific detailedmetadata representation 630 and implemented as network policies of thecloud platform. The cloud platform may further create security groups toimplement network strategies to implement the data center according tothe declarative specification.

The cloud platform independent declarative specification specifiesdependencies between services, for example, start dependencies for eachservice listing all services that should be running when a particularservice is started. The data center generation module 220 generates thecloud platform specific detailed metadata representation of the datacenter that includes information describing these dependencies such thatthe instructions for deploying the service ensure that the cloudplatform starts the services in an order specified by the dependenciessuch that for each service, the services required to be started beforethe service are running when the service is started. Accordingly, thedependencies between services represent a dependency graph and the cloudplatform starts running the services in an order determined based on thedependency graph such that if service A depends on service B, theservice B is started before service A is started.

The data center generation module 220 creates trust relationshipsbetween user accounts that allow services to access other services viasecure communication channels. These trust relationships are generatedusing substrate specific instructions generated based on the declarativespecification, for example, based on outbound access attributesspecified for services. The data center generation module 220 sendsinstructions to the cloud platform to create network policies based oncloud platform specific mechanisms that control the interactions andaccess across service groups and services, for example, as specified bythe constructs of the declarative specification such as outbound access,security groups, security policies and so on.

The data center generation module 210 deploys the cloud platformspecific metadata representation 630 on the specific target cloudplatform for which the representation was generated. The data centergeneration module 210 may perform various validations using thegenerated metadata representations, including policy validations, formatvalidations, and so on.

The cloud platform independent declarative specification 610 may bereferred to as a declared data center representation, cloud-platformindependent detailed metadata representation 620 referred to as aderived metadata representation of the data center, and cloud platformspecific metadata representation 630 referred to as a hydrated metadatarepresentation of the data center.

Overall Process for Deployment of Software Artifacts on a Datacenter

FIG. 7 shows the overall process for generating pipelines for deploymentof software artifacts on datacenters configured on a cloud platformaccording to an embodiment. The datacenter generation module generates710 one or more datacenters on a target cloud platform. Each datacenteris generated from a cloud platform independent declarative specificationand has a hierarchy of datacenter entities.

The software release management module 230 generates 720 a cloudplatform independent master pipeline. In an embodiment, the cloudplatform independent master pipeline includes stages corresponding toenvironments of the datacenters, for example, development environment,test environment, canary environment, and production environment. Themaster pipeline composes a sequence of progressive and/or conditionaldeployment across various environments such as development environment,test environment, staging environment, or production environment. Themaster pipeline may be triggered by delivery of the image for a softwareartifact and includes stages or instructions to deploy the build inenvironments of type development. The software artifact that is built isconditionally promoted to one or more test environments, followed by oneor more canary environments before eventually getting deployed toproduction environments. The master pipeline may be customized by users,for example, service owners to represent a specific orchestration acrossenvironments. The master pipeline may be customized to capture specificpromotion criteria for moving from one stage to next. For example,different tenants of the multi-tenant system may customize the masterpipeline in a different manner. In an embodiment, the master pipeline bydefault uses the latest version of software for a software artifact fora service and builds and deploys the version across variousenvironments. The user can use the artifact version map to ensure that aspecific version of a software artifact is deployed on specificdatacenter entities.

In an embodiment, each service deployed in the datacenter has a cloudplatform independent master pipeline generated from the datacenterentities as defined by the declarative specification of the datacenter,for example, master pipeline for datacenter instances, master pipelinefor service groups, master pipeline for cells, master pipeline forservices, and so on. The master pipelines may be triggered on deliveryof images of software artifacts. The master pipelines may implement aservice owner-controlled continuous deployment. The master pipelines mayimplement datacenter instance owner-owned or release owner-ownedon-demand deployment.

Certain portions of the master pipeline may be customized by the users,for example, by tenants of a multi-tenant system that are deployingservices on a datacenter. For example, the promotion decision pipelinemay be customized by a tenant to determine which test cases are executedand what threshold is The software release management module 230receives 730 customizations to logic for promoting a software artifactfrom one stage to another stage of the cloud platform independent masterpipeline.

The software release management module 230 compiles 740 the cloudplatform independent master pipeline to generate a cloud platformspecific detailed deployment pipeline that is specific to the hierarchyof datacenter entities of each datacenter as specified by the cloudplatform independent declarative specification for the datacenter.

The software release management module 230 further receives 750 code forreleasing one or more features of services deployed on the datacenter.The software release management module 230 executes 760 the cloudplatform specific detailed deployment pipeline to deploy softwareartifacts based on the received code.

FIG. 8 illustrates an example master pipeline 800 according to anembodiment. A master pipeline represents a sequence of stages thatrepresent progressive conditional deployment across various datacenterenvironments. FIG. 8 shows stages for different environments ofdatacenter including development environment, test environment, canaryenvironment, and production environment. Each stage further represents apipeline that is executed for that stage. Accordingly, the masterpipeline 800 includes a development environment pipeline 810 which feedsinto a test environment pipeline 820, which feeds into a canaryenvironment pipeline 830, which feeds into production environmentpipeline 840.

The pipeline at each stage is a hierarchical pipeline comprising lowerlevel pipelines. For example, the development environment pipeline 810comprises a development master pipeline that feeds into datacenterpipelines D11, D12, . . . , depending on the number of datacentersspecified as having development environment in the declarativespecification of the datacenters.

The test environment pipeline 820 comprises a test master pipeline thatfeeds into datacenter pipelines D21, D22, . . . , depending on thenumber of datacenters specified as having test environment in thedeclarative specification of the datacenters.

The canary environment pipeline 820 comprises a canary master pipelinethat feeds into datacenter pipelines D31, D32, . . . , depending on thenumber of datacenters specified as having canary environment in thedeclarative specification of the datacenters.

The production environment pipeline 820 comprises a production masterpipeline that feeds into datacenter pipelines D21, D22, . . . ,depending on the number of datacenters specified as having testenvironment in the declarative specification of the datacenters.

Each environment pipeline 810, 820, 830 includes a promotion decisionpipeline 815 a, 815 b, 815 c respectively. The outputs of the datacenterpipelines of the environment pipeline are collected by the promotiondecision pipeline 815 that determines whether the software artifact isready for promotion to the next stage. The promotion decision pipeline815 may determine based on test case results obtained by the datacenterswhether the software artifact for the service is promoted to the nextstage. For example, if more than a threshold test cases are passed, thepromotion decision pipeline 815 promotes the software artifact to thenext stage. The last environment stage, for example, the productionenvironment pipeline may not have a promotion decision pipeline sincethere is no subsequent stage to which the software artifact needs to bepromoted. As shown in FIG. 8, the promotion decision pipeline 815 a ofdevelopment environment pipeline determines whether to promote thesoftware artifact from development stage to test stage; the promotiondecision pipeline 815 b of test environment pipeline determines whetherto promote the software artifact from test stage to canary stage, andthe promotion decision pipeline 815 c of canary environment pipelinedetermines whether to promote the software artifact from canary stage toproduction stage.

A master pipeline comprises multiple pipelines, for example, aprovisioning pipeline for provisioning resources of the target cloudplatform and a deployment pipeline for deploying a software artifact ona data center entity. Each pipeline comprises a sequence of stages, eachstage representing one or more actions that need to be performed by thetarget cloud platform towards provisioning and deploying of the datacenter. The data center generation module 210 generates detailedpipelines for deploying versions of software artifacts on datacenterentities.

In an embodiment, the pipeline generator module 320 generates detailedpipelines using pipeline templates that include variables. A pipelinetemplate is converted into a pipeline by providing specific values ofthe variables in the pipeline. The process of generating a pipeline froma template is referred to as hydration of the pipeline template. Apipeline template contains templating expressions used as placeholdersfor actual values used in the deployment. For example, a templatingexpression may be replaced by target specific parameter values orexpressions. Multiple pipeline instances may be generated by hydratingthe pipeline template for different targets. The template variablesrepresent parameters that may be replaced with specific values for agiven target to generate a pipeline instance specific to that target.For example, a template variable “account id” may be replaced with anactual value of account id, for example, “12345” during hydration.

In one embodiment, the pipeline generator module 320 generates pipelinesin a hierarchical fashion based on the hierarchy of the data centerentities of the data center. For example, the data center comprises datacenter entities of different types including data centers, servicegroups, services, and so on. A data center entity may include one ormore child data center entities. For example, a data center includes oneor more service groups as child data center entities. A service groupincludes one or more services as child data center entities.Accordingly, the data center generation module 210 starts at a datacenter entity at a level of the hierarchy and generates pipelines ofdata center entities below that level. For example, the pipelinegenerator module 320 starts at the data center level and generatespipelines for service groups within the data center. For each servicegroup, the pipeline generator module 320 generates pipelines forservices within the service group.

The process for executing pipelines according to one embodiment is asfollows. The software release deployment module 230 receives a requestto deploy a software artifact on a set of data center entities in thetarget cloud platform. The software release deployment module 230executes the master pipeline for one or more datacenters. The softwarerelease deployment module 230 executes the aggregate pipelines for eachservice group of each datacenter. The aggregate pipeline comprisespipelines for services within the service group. For each service withineach service group, the pipeline is executed by executing all the stagesof the pipeline. The execution of the provisioning pipelines results inprovisioning of the resource for a service and the deployment pipelinecauses deployment of the service in the target cloud platform.

FIG. 9 shows the overall process executed by a stage for an environmentof the master pipeline on a cloud platform according to an embodiment.The steps 910, 920, 930, 940, and 950 may be performed by eachenvironment pipeline 810, 820, 830. The production environment pipeline3 may execute only steps 910 and 920. The steps shown in FIG. 9 may beperformed for one service or for multiple services specified using amanifest file.

The environment pipeline for an environment E includes instructions todeploy 910 the software on a set of datacenter entities, for example, aset of datacenter entities specified as having the environment E. In anembodiment, the software artifact is generated by compiling source codefor a service. The source code may be obtained from a version controlsoftware. The set of datacenter entities may include datacenterinstances, service groups, cells, services, or any combination of these.

The environment pipeline for the environment E further includesinstructions for running 920 tests for testing the deployed softwareartifact on the set of datacenter entities. The environment pipeline forthe environment E further includes instructions for evaluating 930 thetest results against promotion criteria, for example, using thepromotion decision pipeline 815. If the promotion criteria are notsatisfied, the steps 910, 920, 930, and 940 may be repeated using arevised software artifact, for example, a software artifact generatedfrom source code that includes fixes for certain defects identifiedduring the testing 920. The environment pipeline for the environment Efurther includes instructions for proceeding 950 to the next stage ifthe promotion criteria are satisfied.

In an embodiment, the master pipeline comprises a hierarchy ofpipelines. The hierarchy comprises multiple levels and pipelines at aparticular level include pipelines of the next lower level as childrenpipelines. For example, at the highest level of hierarchy the masterpipeline includes a release master pipeline that deploys a set ofservices related to a product. The next level of hierarchy includesservice master pipelines that represent al deployments of a particularservice across various environments. The next level of hierarchy mayinclude service group master pipelines followed by service masterpipelines.

FIG. 10 shows an example master pipeline according to an embodiment. Themaster pipeline is a hierarchical pipeline where each stage of apipeline may comprise a pipeline with detailed instructions forexecuting the stage. The master pipeline hierarchy may mirror thedatacenter hierarchy. For example, the top level of the master pipelinerepresents a sequence of stages for different environments. Eachenvironment may include one or more pipelines for datacenter instancesor pipelines for other types of datacenter entities. A datacenterinstance pipeline 1010 may include service group pipelines 1020. Eachservice group pipeline 1020 may include one or more service pipelines1030. A datacenter instance pipeline 1010 may include cell pipelines1025, each cell pipeline 1025 comprising one or more service pipelines1030. The service pipeline 1030 may comprise stages, each stagerepresenting a pipeline representing instructions for deploying theservice for specific environments. The lowest level pipeline or the leaflevel pipeline in the hierarchy is referred to as a unit pipeline andmay include detailed service specific instructions for performing anoperation related to a service. For example, deployment for a servicemay include pre-deployment steps, deployment steps, post deploymentsteps, and post deployment test and validation step. A pipeline that isnot a leaf level pipeline and has one or more child pipeline is anaggregate pipeline that orchestrates executions of the child pipelines.

A master pipeline may be driven by pull requests that occur a versioncontrol system for software receives a request for considering changescommitted to an external repository for inclusion in a project's mainrepository. Accordingly, the master pipeline is automatically triggeredwhen a pull request is received and deploys a software artifact based onthe latest software version for which the pull request is received. Themaster pipeline performs continuous delivery of software artifacts basedon pull requests. The master pipeline may be driven based on anon-demand manner, for example, by invoking a request using applicationprogramming interface (API) of the deployment module 210. The on-demanddeployment based on master pipelines may be requested for any set ofservices and for any version for a given service as specified using theAPI. The master pipeline may be invoked to request a rollback from thecurrent version to a previous version or a rollforward from thecurrently deployed version to a more recent version.

In an embodiment, the deployment module 210 creates a service masterpipeline for each service. These pipelines get triggered when a pullrequest is received for a repository of the software. The deploymentmodule 210 receives pipeline templates from users for specific services.These pipeline templates include detailed instructions for testing,validation, build, etc. for specific services. The datacenter generationmodule 220 receives a cloud platform independent declarativespecification for one or more datacenters. The datacenter generationmodule 220 generates (or configures) datacenters according to thereceived cloud platform independent declarative specifications. Thedeployment module 210 receives promotion decision 815 pipelines. Thepromotion decision 815 pipelines are integrated into the overall masterpipeline.

The pipeline generator creates all pipelines for each datacenter fromthe templates and combines them via master pipelines in a hierarchicalfashion, for example, as illustrated in FIG. 10. In an embodiment, thepipeline generator generates service pipelines for individual services;the pipeline generator generates cell master pipelines to invoke theservice pipelines; the pipeline generator generates service group masterpipelines to invoke cell master pipelines; the pipeline generatorgenerates datacenter instance master pipelines to invoke service grouppipelines; the pipeline generator generates a service master pipeline toinvoke the datacenter instance master pipelines.

Following is a snippet of a master pipeline showing various stages. Eachstage may specify attributes including a stage name, a type of pipeline,a stage type (e.g., master deployment pipeline or promotion pipeline),prior stages, and so on.

{  “stages”: [   {    “stage_name”: “Artifact version map for serviceSVC”,    “stage_type”: “version_map”,    “prior_stage_ids”: [ ]   },   {   “pipeline_type”: “env-type-aggregate”,    “template_name”:“deploy_dev.j2”,    “stage_name”: “Deploy to dev env”,    “stage_type”:“master_deployment_pipeline”,    “prior_stage_ids”: [  “Artifact versionmap for service SVC”  ] }, {    “promote_to”: “test”,   “template_name”: “promote.j2”,    “pipeline_type”: “promotion”,   “stage_name”: “Promote to test env”,    “stage_type”: “promotion”,   “prior_stage_ids”: [  “Deploy to dev env”  ]   },   {   “pipeline_type”: “env-type-aggregate”,    “template_name”:“deploy_test.j2”,    “stage_name”: “Deploy to test env”,   “stage_type”: “master_deployment_pipeline”,    “prior_stage_ids”: [ “Promote to test env”  ]   },   {    “promote_to”: “stage”,   “template_name”: “promote.j2”,    “pipeline_type”: “promotion”,   “stage_name”: “Promote to staging env”,    “stage_type”: “promotion”,   “prior_stage_ids”: [  “Deploy to test env”  ]   },   {   “promote_to”: “stage”,    “template_name”: “promote.j2”,   “pipeline_type”: “promotion”,    “stage_name”: “Promote to stagingenv”,    “stage_type”: “promotion”,    “prior_stage_ids”: [  “Deploy totest env”  ]   } ...

As shown in the examiner master pipeline, the first stage is an artifactversion map. The next stage is a master deployment pipeline fordeploying to development environment. The next stage is a promotionpipeline for determining whether the software artifact can be promotedto the next stage. The next stage is a master deployment pipeline fordeploying to test environment. The next stage is a promotion pipelinefor determining whether the software artifact can be promoted to thenext stage that is staging environment.

Software Artifact Version Map

In an embodiment, the deployment module 210 receives an artifact versionmap that associates various software artifacts and their versions withdatacenter entities. The artifact version map provides a declarativespecification of the specific versions of software artifacts that needto be deployed for services in different datacenter entities. Eachdatacenter entity may be uniquely identified based on its locationwithin the datacenter hierarchy as specified by the declarativespecification of the datacenter. For example, for a service, a softwarelibrary may act as a software artifact. The software artifact may havemultiple versions, for example, V1, V2, V3, and so on. The artifactversion map may specify that version V1 needs to be deployed indatacenter entities C1 and C2 and version V2 needs to be deployed indatacenter entities C3 and C4. The deployment module 210 generatesmaster pipelines and instructions that ensure that the appropriatesoftware artifact versions are deployed in the datacenter entities asspecified in the artifact version map.

In an embodiment, the artifact version map is specified as a JSON(Javascript object notation) file, a YAML file, or a file using anyother syntax for representing nested objects. The artifact version mapmay comprise a set of <service>:<version> key pairs that are associatedwith various datacenter entities distributed across a hierarchy of adatacenter. The artifact version map key pairs act as whitelists forcorresponding pipelines. If a key for a service is not included into anartifact version map, all pipelines for that service are excluded duringexecution of the pipeline. Different artifact version maps may beapplied to the same master pipeline resulting in different servicesbeing included/excluded during execution of the master pipeline.

Following is an example artifact version map. The artifact version mapspecifies environment types using the attribute “env_types”. In thefollowing example, the environment type development is specified. Theenvironment type may include one or more datacenter instances; adatacenter instance may include one or more service groups, a servicegroup may include one or more services. In the following example, thesoftware artifact name is specified as library1 and version as version1and is associated with the service instance instance001. However, thesoftware artifact name and version may be associated with any level ofdatacenter entity in the hierarchy. For example, of the softwareartifact name and version is specified or a service group, the softwareartifact name and version is applicable to all services within theservice group unless the software artifact name and version isoverridden with different values of the software artifact name andversion specified for a particular service instance within the servicegroup. Similarly, the software artifact name and version can bespecified for a datacenter instance and is applicable to all servicegroups or cells within the datacenter instance unless an overridingvalue is specified for a service group.

{  “name”: “artifact_version_map1”,  “schema_version”: “0.1”, “release_label”: “release1.1”,  “deployments”: {   “env_types”: [    {    “name”: “development”,     “datacenter_instances”: [      {      “name”: “datacenter1”,       “service_group”: [        {        “name”: “service_group1”,         “services”: [          {          “service_instance”: “instance001”,           “name”:“service1”,           “versions”: [            {            “software_artifact_name”: “library1”,             “version”:“version1”            }           ]          }         ]        }      ]      }     ]    }   ],  } }

In an embodiment, the artifact version map specifies a datacenter entityusing a full path of the datacenter entity, for example,“stagger_group1/datacenter1/service_group2/service1”. In an embodiment,the artifact version map specifies a set of datacenter entities usingregular expressions in the full path of the datacenter entity. Forexample, a full path that includes service_group[?] includesservice_group1, service_group2, service_group3, and so on.

Following is an example of an artifact version map specifying regularexpressions to define a set of services. The environment types arespecified as dev and test and the datacenter entities in the full pathincluding datacenter instances and service groups are specified aswildcards and service instances are specified as “service*”.Accordingly, for all datacenter instances for dev and test environments,for all service groups, for services names matching service*, theversion V1 of application app1 will be deployed.

  env_types:  - name: “dev | test”   datacenter instances:    - name:“(.*)”     service_group:      - name: “(.*)”       services:        -service_instance: “service*”         name: “app1”          versions:          version: “V1”

In some embodiments, the artifact version map may specify parametersused by pipelines. Accordingly, the specified parameters will beapplicable to a stagger group for which the parameter is specified.

FIG. 11 shows an example master pipeline for a datacenter according toan embodiment. As shown in FIG. 11, master pipeline is executed over adeployment group (also referred to as a stagger group) referring tocollections of datacenter entities as defined by a declarativespecification. The artifact version map may specify a stagger groupattribute to define a set of datacenter entities that may be associatedwith a specific artifact version. A datacenter entity may be identifiedby specifying a path from a root node in a hierarchy of a datacenter,for example, the datacenter instance. As shown in FIG. 11, the masterpipeline defines various stages for different environments. Each stageperforms actions on a set of datacenter entities referred to asdeployment group 1110. FIG. 11 shows a development deployment group 1110a comprising datacenter entities including service groups S11, S12, andS13; a test deployment group 1110 b comprising datacenter entitiesincluding service groups S21, S22, and S23; and a production deploymentgroup 1110 b comprising datacenter entities including service groupsS31, S32, S33, and S34. Results of testing on the datacenter entities ofthe development deployment group 1110 a are evaluated 1120 a todetermine whether the software artifacts tested in the developmentdeployment group 1110 a are promoted to the test deployment group 1110b. Similarly, results of testing on the datacenter entities of the testdeployment group 1110 a are evaluated 1120 b to determine whether thesoftware artifacts tested in the test deployment group 1110 a arepromoted to the production deployment group 1110 c.

FIG. 12 illustrates how the execution of the master pipeline is modifiedbased on an artifact version map according to an embodiment. Theartifact version map associates versions of software artifacts withdatacenter entities. The system modifies the detailed pipelines executedon the datacenter entities to ensure that actions related to theappropriate versions of software artifacts are performed on eachdatacenter entities. For example, FIG. 12 shows versions V1 and V2 of asoftware artifact for a service SVC1. Version V1 is associated withservice groups S11, S23, and S31 and version V2 is associated withservice groups S11, S21, and S32. Assume that the master pipeline isdeploying versions of the service SVC1 on the datacenter. Accordingly,the system modifies the pipeline executed to deploy the version V1 onservice groups S11, S23, and S31 and version V2 on service groups S11,S21, and S32. In an embodiment, the system introduces a version mapfilter before stages of the master pipeline shown in FIG. 11. Theexecution of the master pipeline selects the versions of softwareartifacts for each datacenter entity based on the version map filter.For example, the version map filter includes instructions to skipservice groups S13, S22, S33, and S34 for this particular action. Theversion map filter specifies that version V1 is deployed on servicegroups S11, S23, and S31 and version V2 is deployed on service groupsS11, S21, and S32.

FIG. 13 shows the overall process for deployment of software artifactson datacenters configured on a cloud platform according to anembodiment. The datacenter generation module generates 1310 one or moredatacenters on a target cloud platform. Each datacenter is generatedfrom a cloud platform independent declarative specification and has ahierarchy of datacenter entities.

The software release management module 230 receives as input, anartifact version map that maps datacenter entities to versions ofsoftware artifacts. The software release management module 230 alsoreceives 1330 as input, a cloud platform independent master pipeline.

The software release management module 230 compiles 1340 the cloudplatform independent master pipeline in conjunction with the artifactversion map to generate a cloud platform specific detailed pipeline. Inan embodiment, the generated cloud platform specific detailed pipelineincludes artifact version map filters before certain stages to determinewhether certain stages should be enabled or disabled according to theartifact version map.

The software release management module 230 further receives 1350 codefor releasing one or more features of services deployed on thedatacenter. For example, the code may represent source code obtainedfrom a version control management system storing source coderepositories to which changes are submitted by developers. The softwarerelease management module 230 executes 1360 the cloud platform specificdeployment pipeline to deploy software artifacts based on the receivedcode.

The artifact version map and master pipelines can be used to orchestratevarious types of operations related to continuous delivery of softwareartifacts in a cloud-based datacenter. The artifact version map and themaster pipelines can be configured to perform aggregate retry operationsfor a service or a service group or any datacenter entity. The artifactversion map includes configurations of retry operations for a datacenterentity, including the retry strategy, a threshold number of retries toperform in case of failure to execute a stage of a pipeline, whetherconfirmation from a user is required before retrying or retry isperformed automatically, and so on. For example, a retry strategy may bea fixed backoff strategy that pauses execution for a fixed period oftime before retrying. Other retry strategies may be configured usingartifact version map and master pipelines. In an embodiment, thepipeline generator introduces an invoke retrier stage within anaggregate pipeline to trigger a retry strategy if a previous pipelinestage fails. The retry strategy and configuration parameters specifiedfor a datacenter entity applies to all datacenter entities and serviceswithin the datacenter entity unless the value is overridden for a nesteddatacenter entity.

Computer Architecture

FIG. 14 is a high-level block diagram illustrating a functional view ofa typical computer system for use as one of the entities illustrated inthe environment 100 of FIG. 1 according to an embodiment. Illustratedare at least one processor 1402 coupled to a chipset 1404. Also coupledto the chipset 1404 are a memory 1406, a storage device 1408, a keyboard1410, a graphics adapter 1412, a pointing device 1414, and a networkadapter 1416. A display 1418 is coupled to the graphics adapter 1412. Inone embodiment, the functionality of the chipset 1404 is provided by amemory controller hub 1420 and an I/O controller hub 1422. In anotherembodiment, the memory 1406 is coupled directly to the processor 1402instead of the chipset 1404.

The storage device 1408 is a non-transitory computer-readable storagemedium, such as a hard drive, compact disk read-only memory (CD-ROM),DVD, or a solid-state memory device. The memory 1406 holds instructionsand data used by the processor 1402. The pointing device 1414 may be amouse, track ball, or other type of pointing device, and is used incombination with the keyboard 1410 to input data into the computersystem 200. The graphics adapter 1412 displays images and otherinformation on the display 1418. The network adapter 1416 couples thecomputer system 1400 to a network.

As is known in the art, a computer 1400 can have different and/or othercomponents than those shown in FIG. 14. In addition, the computer 1400can lack certain illustrated components. For example, a computer system1400 acting as a multi-tenant system 110 may lack a keyboard 1410 and apointing device 1414. Moreover, the storage device 1408 can be localand/or remote from the computer 1400 (such as embodied within a storagearea network (SAN)).

The computer 1400 is adapted to execute computer modules for providingthe functionality described herein. As used herein, the term “module”refers to computer program instruction and other logic for providing aspecified functionality. A module can be implemented in hardware,firmware, and/or software. A module can include one or more processes,and/or be provided by only part of a process. A module is typicallystored on the storage device 1408, loaded into the memory 1406, andexecuted by the processor 1402.

The types of computer systems 1400 used by the entities of a systemenvironment can vary depending upon the embodiment and the processingpower used by the entity. For example, a client device may be a mobilephone with limited processing power, a small display 1418, and may lacka pointing device 1414. A multi-tenant system or a cloud platform, incontrast, may comprise multiple blade servers working together toprovide the functionality described herein.

ADDITIONAL CONSIDERATIONS

The particular naming of the components, capitalization of terms, theattributes, data structures, or any other programming or structuralaspect is not mandatory or significant, and the mechanisms thatimplement the embodiments described may have different names, formats,or protocols. Further, the systems may be implemented via a combinationof hardware and software, as described, or entirely in hardwareelements. Also, the particular division of functionality between thevarious system components described herein is merely exemplary, and notmandatory; functions performed by a single system component may insteadbe performed by multiple components, and functions performed by multiplecomponents may instead performed by a single component.

Some portions of above description present features in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are the means used bythose skilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. These operations,while described functionally or logically, are understood to beimplemented by computer programs. Furthermore, it has also provenconvenient at times, to refer to these arrangements of operations asmodules or by functional names, without loss of generality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain embodiments described herein include process steps andinstructions described in the form of an algorithm. It should be notedthat the process steps and instructions of the embodiments could beembodied in software, firmware or hardware, and when embodied insoftware, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The embodiments described also relate to apparatuses for performing theoperations herein. An apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a non-transitory computer readablestorage medium, such as, but is not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, magnetic-optical disks,read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will be apparent to those ofskill in the, along with equivalent variations. In addition, the presentembodiments are not described with reference to any particularprogramming language. It is appreciated that a variety of programminglanguages may be used to implement the teachings of the embodiments asdescribed herein.

The embodiments are well suited for a wide variety of computer networksystems over numerous topologies. Within this field, the configurationand management of large networks comprise storage devices and computersthat are communicatively coupled to dissimilar computers and storagedevices over a network, such as the Internet.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructional purposesand may not have been selected to delineate or circumscribe theinventive subject matter. Accordingly, the disclosure of the embodimentsis intended to be illustrative, but not limiting.

1. A computer implemented method for deploying software artifacts forservices executing in data centers configured on a cloud platform, themethod comprising: accessing a data center configured on a target cloudplatform, the datacenter generated based on a cloud platform independentdeclarative specification comprising a hierarchy of data centerentities, wherein each data center entity comprises one or more of (1) aservice or (2) one or more other data center entities; receiving a cloudplatform independent artifact version map associating data centerentities of the data center with versions of software artifacts targetedfor deployment on the datacenter entities, each software artifactcomprising executable instructions associated with a service configuredfor execution on one or more cloud platforms; generating a cloudplatform specific deployment pipeline for the target cloud platformbased on the cloud platform independent artifact version map, the cloudplatform specific deployment pipeline comprising instructions to buildand deploy appropriate versions of deployment artifacts for services ondata center entities in accordance with the cloud platform independentartifact version map; and transmitting the cloud platform specificdeployment pipeline to the target cloud platform for execution.
 2. Thecomputer implemented method of claim 1, further comprising: generating acloud platform independent deployment pipeline comprising instructionsfor building and deploying software artifacts on the datacenter; andcompiling the cloud platform independent deployment pipeline inconjunction with the cloud platform independent artifact version map. 3.The computer implemented method of claim 2, wherein the cloud platformindependent deployment pipeline comprises a version map filter beforeone or more stages, wherein execution of the cloud platform independentdeployment pipeline causes a version of a software artifact to beselected for a data center entity based on the version map filter. 4.The computer implemented method of claim 1, further comprising:receiving the cloud platform independent declarative specification; andcompiling the cloud platform independent declarative specification togenerate a cloud platform specific data center representation. 5-6.(canceled)
 7. The computer implemented method of claim 1, wherein thecloud platform independent declarative specification comprisesdefinitions of one or more data center instances, each data centerinstance including one or more service groups, wherein each servicegroup comprises a set of services.
 8. The computer implemented method ofclaim 1, wherein the generated instructions comprise one or morepipelines, each pipeline comprising a sequence of stages, each stageperforming one or more actions for deploying a software artifact on adatacenter entity.
 9. A non-transitory computer readable storage mediumfor storing instructions that when executed by a computer processorcause the computer processor to perform steps for deploying softwareartifacts for services executing in data centers configured on a cloudplatform, the steps comprising: accessing a data center configured on atarget cloud platform, the datacenter generated based on a cloudplatform independent declarative specification comprising a hierarchy ofdata center entities, wherein each data center entity comprises one ormore of (1) a service or (2) one or more other data center entities;receiving a cloud platform independent artifact version map associatingdata center entities of the data center with versions of softwareartifacts targeted for deployment on the datacenter entities, eachsoftware artifact comprising executable instructions associated with aservice configured for execution on one or more cloud platforms;generating a cloud platform specific deployment pipeline for the targetcloud platform based on the cloud platform independent artifact versionmap, the cloud platform specific deployment pipeline comprisinginstructions to build and deploy appropriate versions of deploymentartifacts for services on data center entities in accordance with thecloud platform independent artifact version map; and transmitting thecloud platform specific deployment pipeline to the target cloud platformfor execution.
 10. The non-transitory computer readable storage mediumof claim 9, wherein the instructions further cause the processor toperform steps comprising: generating a cloud platform independentdeployment pipeline comprising instructions for building and deployingsoftware artifacts on the datacenter; and compiling the cloud platformindependent deployment pipeline in conjunction with the cloud platformindependent artifact version map.
 11. The non-transitory computerreadable storage medium of claim 10, wherein the cloud platformindependent deployment pipeline comprises a version map filter beforeone or more stages, wherein execution of the cloud platform independentdeployment pipeline causes a version of a software artifact to beselected for a data center entity based on the version map filter. 12.The non-transitory computer readable storage medium of claim 9, whereinthe instructions further cause the processor to perform stepscomprising: receiving the cloud platform independent declarativespecification; and compiling the cloud platform independent declarativespecification to generate a cloud platform specific data centerrepresentation. 13-14. (canceled)
 15. The non-transitory computerreadable storage medium of claim 9, wherein the cloud platformindependent declarative specification comprises definitions of one ormore data center instances, each data center instance including one ormore service groups, wherein each service group comprises a set ofservices.
 16. The non-transitory computer readable storage medium ofclaim 9, wherein the generated instructions comprise one or morepipelines, each pipeline comprising a sequence of stages, each stageperforming one or more actions for deploying a software artifact on adatacenter entity.
 17. A computer system comprising: a computerprocessor; and a non-transitory computer readable storage medium forstoring instructions that when executed by the computer processor, causethe computer processor to perform steps for deploying software artifactsfor services executing in data centers configured on a cloud platform,the steps comprising; accessing a data center configured on a targetcloud platform, the datacenter generated based on a cloud platformindependent declarative specification comprising a hierarchy of datacenter entities, wherein each data center entity comprises one or moreof (1) a service or (2) one or more other data center entities;receiving a cloud platform independent artifact version map associatingdata center entities of the data center with versions of softwareartifacts targeted for deployment on the datacenter entities, eachsoftware artifact comprising executable instructions associated with aservice configured for execution on one or more cloud platforms;generating a cloud platform specific deployment pipeline for the targetcloud platform based on the cloud platform independent artifact versionmap, the cloud platform specific deployment pipeline comprisinginstructions to build and deploy appropriate versions of deploymentartifacts for services on data center entities in accordance with thecloud platform independent artifact version map; and transmitting thecloud platform specific deployment pipeline to the target cloud platformfor execution.
 18. The computer system of claim 17, wherein theinstructions further cause the computer processor to perform stepscomprising: generating a cloud platform independent deployment pipelinecomprising instructions for building and deploying software artifacts onthe datacenter; and compiling the cloud platform independent deploymentpipeline in conjunction with the cloud platform independent artifactversion map.
 19. The computer system of claim 18, wherein the cloudplatform independent deployment pipeline comprises a version map filterbefore one or more stages, wherein execution of the cloud platformindependent deployment pipeline causes a version of a software artifactto be selected for a data center entity based on the version map filter.20. The computer system of claim 17, wherein the instructions furthercause the computer processor to perform steps comprising: receiving thecloud platform independent declarative specification; and compiling thecloud platform independent declarative specification to generate a cloudplatform specific data center representation.
 21. The computerimplemented method of claim 1, further comprising: providing users withaccess to the services running in the data center deployed on the targetcloud platform.
 22. The non-transitory computer readable storage mediumof claim 9, wherein the instructions further cause the computerprocessor to perform steps comprising: providing users with access tothe services running in the data center deployed on the target cloudplatform.
 23. The computer system of claim 17, wherein the cloudplatform independent declarative specification comprises definitions ofone or more data center instances, each data center instance includingone or more service groups, wherein each service group comprises a setof services.
 24. The computer system of claim 17, wherein the generatedinstructions comprise one or more pipelines, each pipeline comprising asequence of stages, each stage performing one or more actions fordeploying a software artifact on a datacenter entity.